The use of bigrams to enhance text categorization
Information Processing and Management: an International Journal
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Improving Patient Opinion Mining through Multi-step Classification
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
| Hi-index | 0.00 |
Current approaches to sentiment analysis assume that the sole discourse function of sentiment-bearing texts is expressivity. However, the persuasive discourse function also utilises expressive language. In this work, we present the results of training supervised classifiers on a new corpus of clinical texts that contain documents with an expressive discourse function, and we test the learned models on a subset of the same corpus containing persuasive texts. The results of this indicate that despite the difference in discourse function, the learned models perform favourably.